FIG. 1 shows an X-ray device of this kind, known from DE 100 37 735 A1, that has a C-arm 2, rotatably mounted on a stand 1, on the ends of which are mounted an X-ray beam 3 and an X-ray image detector 4.
Floor or ceiling mountings can also be used instead of the illustrated stand 1. The C-arm 2 can also be replaced by an electronic C-arm 2 with an electronic coupling of the X-ray beam 3 and X-ray detector 4.
The X-ray detector 4 can be a rectangular or square, flat semiconductor detector that preferably is made from amorphous silicon (aSi).
A patient couch 5 to support the patient to be examined is located in the path of the X-ray beam 3.
In X-ray diagnostics, high-resolution images are required as the basis of a safe and correct diagnosis. The object is to make the smallest detail visible at high quality. In X-ray diagnostics, the image quality is influenced mainly by the administered X-ray dose. The X-ray dose, however, mainly influences the image noise and the contrast of an X-ray image with, very generally speaking, a high X-ray dose producing a noise-free, strong-contrast image.
Just using flat image detectors (FD) alone has no effect on the resolution of an X-ray image. It depends essentially on the pixel resolution of the detector system.
Prior art consists of the use of zoom formats on C-arm systems to produce a high-resolution X-ray image. These methods do not use a complete X-ray image detector for image generation, but instead only a smaller part area so that the image appears enlarged. This process is, however, in the end limited by the existing resolution of the X-ray image amplifier (RBV) or flat detector (FD) and is not able to show anatomical details that are smaller than the physical resolution capability of the X-ray image detector. Furthermore, image interpolation methods that extrapolate the single images to a higher resolution, e.g. using bi-cubic interpolation, are not able to resolve details that are too small and therefore not visible.
The only way to improve the resolution capability is to make an expensive change to the X-ray image detectors of RBV and FD systems. This means that an improved X-ray image detector must offer in the same area 2048×2048 pixels instead of 1024×1024 pixels. This, however places a heavy requirement on detector manufacturers, already at the limits of what is currently technically possible, and on costs that a new image detector involves, not to mention the fact that the area of a single pixel, that reduces with an increase in the resolution, directly influences the X-ray quantum yield and thus, for example, also the noise in the X-ray image.
Altogether, the technical possibilities available for increasing the pixel resolution are very limited.
For this reason, the older patent application DE 10 2005 010 119.4 proposed changes to the source-image distance (SID) for present-day C-arm systems whereby a sequence of low-resolution images with a different source-image distance (SID) are produced, a harmonization of the systems of coordinates of the images is carried out and an image with a higher resolution, called a C-arm superresolution image, is calculated from the images. C-arm systems are, however, generally not the X-ray systems that are used for diagnostic purposes because they are too expensive and have too few features to create a normal X-ray image. The aforementioned C-arm solution, variation of the SID, simply cannot be used with present-day simple systems because with those the SID can generally not be varied.
Also in other areas in which images are taken, for example, with current video and photographic cameras, there is a similar problem. Therefore, it is not technically possible to increase the resolution of photographic cameras to order. In applications in which a greater degree of detail is required in the images, such as for example with satellite pictures and military reconnaissance pictures, methods called “Superresolution” have been known for a fairly long time that take several single pictures and from these calculate a single high-resolution image, such as is for example described in “Advances and Challenges in Super-Resolution” by Sina Farsiu et al., Invited Paper, International Journal of Imaging Systems and Technology, Special Issue on High Resolution Image Reconstruction, Vol. 14, No. 2, pages 47 to 57, 2004.
All that is available in the field of medicine is a description of the application of a superresolution method for the generation of high-resolution MRI images in “Superresolution in MRI: Application to Human White Matter Fiber Tract Visualization by Diffusion Tensor Imaging” by Sharon Peled et al., Magnetic Resonance in Medicine, 45, pages 29 to 35 (2001).
The functional principle of superresolution methods is based on the availability of a sequence of images consisting of several images as an input that can be registered against each other by an affine transformation. With satellite pictures or with video sequences taken by a video camera this affine transformation is, for example, provided by a shift of the scene in the image. This translation adequately meets the requirements of an affine transformation and is very easy to realize.
The general model of superresolution can according to M. Elad et al., “Super-Resolution Reconstruction of Image Sequences” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 9, pages 817 to 834, September 1999, can be described as follows: Images gi of a low resolution of a sequence of images are the result of a projection P of a high-resolution image f on their image plane and a matching of their systems of coordinates by an affine 2-D transformation. Only the images with a low resolution can be observed, the high-resolution image cannot be observed because of the limited facilities of the camera. It therefore follows that the approach functions because, due to the affine transformation, the images gi are, and also must be, located in different systems of coordinates.
The principle of superresolution is now explained using FIG. 2. Each box, both large and small, represents a single pixel or a single image point. FIG. 2 shows a first image 6 with a low resolution with pixels 9 and also a second image 7 with a slightly smaller resolution, shifted in the x and y direction, that by means of a transformation are to be brought to an image 8 with a higher resolution having image points 10. In the high-resolution calculated image 8, the area of the image points 10 is too small, whereas they are too large in the pixels 9 of the low-resolution original images 6 and 7.
The offset of the systems of coordinates required for the superresolution is very easy to create for satellite and video pictures:
For satellite pictures                The satellite itself moves around the earth. The pictures that are taken are therefore offset relative to each other.        
For video pictures                With hand-held cameras a suitable movement is very easy to achieve.That means that in both cases a moving scene of images with low resolution forms the starting point for a high resolution image.        